Search Results (4)

Intraventricular hemorrhage (IVH) is a common complication encountered in extremely-low-birth-weight (ELBW) and very-low-birth-weight (VLBW) premature babies. The neurologic outcome of these patients is influenced by the magnitude of the hemorrhagic process that damages the involved anatomic structures but also by the impaired circulation of cerebrospinal fluid (CSF) through the ventricular system, leading to posthemorrhagic ventriculomegaly (PHVM). Cranial ultrasound (CUS) performed by neonatologists (point-of-care ultrasound—POCUS) facilitates the early diagnosis of IVH and PHVM and can objectively quantify structural alterations. Our aim was to identify the best sonographic criteria to follow-up with ventricular dilatation and predict the need for neurosurgery and neurologic deterioration. We performed a literature review in search of the most relevant ventricular measurements considered by neurosurgeons, neonatologists, and pediatric neurologists to reflect the risk of white matter injury and high intracranial pressure (HIP), thus anticipating neurologic developmental impairment (NDI). The tridimensional picture of ventricular dilatation is best captured if more than one index (ventricular index and anterior horn width) or ratio (Evans ratio, fronto-occipital horn ratio, and fronto-temporal horn ratio) is used. Conclusions: If performed using the correct protocol, serially and comprehensively, CUS is an indispensable tool for the diagnosis and follow-up of neurologic complications of preterm babies, and it can make a difference in guiding adequate intervention and improving long-term developmental outcomes. Full article

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques—including statistical, regularization, deep learning, decision trees, and Bayesian methods—were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation. Full article

The survival rate of preterm infants is increasing as a result of technological advances. The incidence of intraventricular hemorrhages (IVH) in preterm infants ranges from 25% to 30%, of which 30% to 50% are severe IVH (Volpe III-IV, Volpe III is defined as intraventricular bleeding occupying more than 50% of the ventricular width and acute lateral ventricle dilatation, Volpe IV is defined as intraventricular hemorrhage combined with venous infarction) and probably lead to posthemorrhagic ventricular dilatation (PHVD). Severe IVH and subsequent PHVD have become the leading causes of brain injury and neurodevelopmental dysplasia in preterm infants. This review aims to review the literature on the diagnosis and therapeutic strategies for PHVD and provide some recommendations for management to improve the neurological outcomes. Full article

Post-hemorrhagic ventricular dilatation (PHVD) is characterized by a build-up of cerebral spinal fluid (CSF) in the ventricles, which increases intracranial pressure and compresses brain tissue. Clinical interventions (i.e., ventricular taps, VT) work to mitigate these complications through CSF drainage; however, the timing of these procedures remains imprecise. This study presents Neonatal NeuroMonitor (NNeMo), a portable optical device that combines broadband near-infrared spectroscopy (B-NIRS) and diffuse correlation spectroscopy (DCS) to provide simultaneous assessments of cerebral blood flow (CBF), tissue saturation (S_tO₂), and the oxidation state of cytochrome c oxidase (oxCCO). In this study, NNeMo was used to monitor cerebral hemodynamics and metabolism in PHVD patients selected for a VT. Across multiple VTs in four patients, no significant changes were found in any of the three parameters: CBF increased by 14.6 ± 37.6% (p = 0.09), S_tO₂ by 1.9 ± 4.9% (p = 0.2), and oxCCO by 0.4 ± 0.6 µM (p = 0.09). However, removing outliers resulted in significant, but small, increases in CBF (6.0 ± 7.7%) and oxCCO (0.1 ± 0.1 µM). The results of this study demonstrate NNeMo’s ability to provide safe, non-invasive measurements of cerebral perfusion and metabolism for neuromonitoring applications in the neonatal intensive care unit. Full article